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Mapping winter vegetation quality coverage is a challenge problem of remotesensing. This is due to the cloud coverage in winter period, leading to useradar rather than optical images. The objective of this paper is to provide abetter understanding of the capabilities of radar Sentinel-1 and deep learningconcerning about mapping winter vegetation quality coverage. The analysispresented in this paper is carried out on multi-temporal Sentinel-1 data overthe site of La Rochelle, France, during the campaign in December 2016. Thisdataset were processed in order to produce an intensity radar data stack fromOctober 2016 to February 2017. Two deep Recurrent Neural Network (RNN) basedclassifier methods were employed. We found that the results of RNNs clearlyoutperformed the classical machine learning approaches (Support Vector Machineand Random Forest). This study confirms that the time series radar Sentinel-1and RNNs could be exploited for winter vegetation quality cover mapping.

Deep Recurrent Neural Networks for mapping winter vegetation quality coverage via multi-temporal SAR Sentinel-1